Typo in Chapter 1 #25

Author: souzatharsis

tamingllms/_build/.doctrees/environment.pickle

@@ -171,7 +171,7 @@
     "from docling.document_converter import DocumentConverter\n",
     "\n",
     "converter = DocumentConverter()\n",
-    "forecast_result_docling = converter.convert(source).document.export_to_markdown()"
+    "forecast_result_docling = converter.convert(FORECAST_FILE_PATH).document.export_to_markdown()"
    ]
   },
   {

tamingllms/_build/.doctrees/notebooks/input.doctree

@@ -408,7 +408,7 @@ <h3><a class="toc-backref" href="#id331" role="doc-backlink"><span class="sectio
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">docling.document_converter</span> <span class="kn">import</span> <span class="n">DocumentConverter</span>
 
 <span class="n">converter</span> <span class="o">=</span> <span class="n">DocumentConverter</span><span class="p">()</span>
-<span class="n">forecast_result_docling</span> <span class="o">=</span> <span class="n">converter</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="n">source</span><span class="p">)</span><span class="o">.</span><span class="n">document</span><span class="o">.</span><span class="n">export_to_markdown</span><span class="p">()</span>
+<span class="n">forecast_result_docling</span> <span class="o">=</span> <span class="n">converter</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="n">FORECAST_FILE_PATH</span><span class="p">)</span><span class="o">.</span><span class="n">document</span><span class="o">.</span><span class="n">export_to_markdown</span><span class="p">()</span>
 </pre></div>
 </div>
 </div>

tamingllms/_build/html/_sources/notebooks/input.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "8b162760",
+   "id": "54449f64",
    "metadata": {},
    "source": [
     "(intro)=\n",

tamingllms/_build/html/notebooks/input.html

@@ -171,7 +171,7 @@
     "from docling.document_converter import DocumentConverter\n",
     "\n",
     "converter = DocumentConverter()\n",
-    "forecast_result_docling = converter.convert(source).document.export_to_markdown()"
+    "forecast_result_docling = converter.convert(FORECAST_FILE_PATH).document.export_to_markdown()"
    ]
   },
   {

tamingllms/_build/jupyter_execute/markdown/intro.ipynb

@@ -117,7 +117,7 @@ \subsection{Structured Data Extraction\index{Structured output}}
 from docling.document_converter import DocumentConverter
 
 converter = DocumentConverter()
-forecast_result_docling = converter.convert(source).document.export_to_markdown()
+forecast_result_docling = converter.convert(FORECAST_FILE_PATH).document.export_to_markdown()
 \end{minted}
 How similar are the two results? We can use use Levenshtein distance\index{Levenshtein distance}~\sidecite{10.5555/1822502} to measure the similarity between the two results. We will also calculate a naive score using the \texttt{SequenceMatcher} from the \texttt{difflib} package, which is a simple measure of similarity between two strings based on the number of matches in the longest common subsequence.
 

tamingllms/_build/jupyter_execute/notebooks/input.ipynb

@@ -171,7 +171,7 @@
     "from docling.document_converter import DocumentConverter\n",
     "\n",
     "converter = DocumentConverter()\n",
-    "forecast_result_docling = converter.convert(source).document.export_to_markdown()"
+    "forecast_result_docling = converter.convert(FORECAST_FILE_PATH).document.export_to_markdown()"
    ]
   },
   {